ZILONEX GOVERN

AI Governance
for Healthcare

ISO 42001, EU AI Act classification, dataset lineage, and post-market monitoring for clinical AI and health-tech teams.

AI governance for healthcare is the AI-specific layer that sits alongside a medical-device quality management system — capturing model registries, dataset lineage, EU AI Act high-risk obligations, ISO 42001 conformance, and post-market performance monitoring for the AI components of a clinical product.

Clinical AI is high-risk by default

AI systems that are themselves medical devices, or are safety components of medical devices, sit inside the EU AI Act's high-risk category. Diagnostic imaging, clinical decision support, triage, and risk stratification models generally qualify. The obligations that follow — technical documentation, data quality, human oversight, accuracy and robustness, transparency, and post-market monitoring — apply on top of, not instead of, existing medical-device requirements.

Zilonex Govern classifies each registered clinical model, captures the reasoning behind the tier, and maps each obligation to a tracked control.

Dataset lineage for medical data

Regulators and enterprise buyers ask the same three questions about clinical AI: what data was it trained on, how representative is that data, and how do we know it still performs on the population we serve. Dataset lineage answers the first two. Post-market monitoring answers the third.

Each model in the registry links to a training-data record: dataset identifier, version, source description, consent and legal basis, annotation methodology, cohort composition, and known limitations. When cohort composition changes materially, a new model version is expected, and Zilonex Govern makes that expectation explicit in the workflow.

Post-market monitoring as a first-class control

Static conformance is not enough for clinical AI. Performance drifts as populations, imaging protocols, and clinical workflows evolve. Zilonex Govern treats post-market monitoring as a defined control: cadence, methodology, acceptance criteria, escalation path, and evidence storage all live inside the platform.

The platform is a system of record, not a real-time evaluation engine. It complements whatever clinical performance pipeline your team already runs by holding the policies, the reviews, and the evidence trail.

Alongside ISO 13485 and ISO 14971

Health-tech quality teams already run medical-device quality management under ISO 13485 and risk management under ISO 14971. Zilonex Govern is deliberately not a replacement for either. It is the AI-specific governance layer that adds the model registry, ISO 42001 conformance mapping, EU AI Act classification, dataset lineage, and AI-specific risks that classical device standards were not designed to address.

Auditors read the exported pack. Engineers keep working in the SDK. The QMS remains the QMS.

Model cards enterprise health systems ask for

Enterprise health-system procurement now routinely asks for a model card per AI feature. Zilonex Govern generates the card from the underlying registry entry: intended use, contraindicated use, training data description, performance by cohort, known limitations, and the monitoring plan. Cards export as PDF for procurement packs and as JSON for integration into enterprise catalogues.

Frequently asked questions

Is clinical AI high-risk under the EU AI Act?

Almost always. AI systems that are safety components of medical devices, or that are themselves medical devices requiring third-party conformity assessment, are classified as high-risk. That covers diagnostic imaging models, clinical decision support, triage tools, and risk-stratification algorithms. Zilonex Govern captures the classification, the reasoning, and the mapped obligations for each registered model.

How does this relate to ISO 13485 and ISO 14971?

ISO 13485 addresses quality management for medical devices and ISO 14971 addresses risk management for medical devices. Zilonex Govern does not replace either — it provides the AI-specific governance layer that sits alongside them. Your quality management system remains the source of truth for device processes. Zilonex Govern manages the AI model registry, ISO 42001 conformance, EU AI Act classification, dataset lineage, and post-market performance evidence for the AI components.

What does dataset lineage look like for medical imaging?

Each registered model links to a training-data pointer that captures the dataset identifier, version, source description, consent basis, annotation methodology, and known cohort limitations. When a reviewer asks "what data was this model trained on, how was it labeled, and who signed off on its representativeness," the answer is attached to the model, not sitting in a shared drive.

How is post-market monitoring handled?

The risk register lets you define a monitoring cadence per model (for example, quarterly performance evaluation on a held-out cohort, monthly drift checks, incident-driven review). Reviews are logged, deviations are captured, and any incidents feed a corrective-action workflow. It is a system of record — not a real-time monitoring pipeline — so it complements whatever clinical performance monitoring stack your team runs.

Do we need a model card for every clinical model?

Enterprise health-system procurement teams and regulators increasingly expect one. Zilonex Govern generates a model card from the registry entry: intended use, contraindicated use, training data description, performance by cohort, known limitations, and monitoring plan. It exports as PDF and as machine-readable JSON.

How fast can a small health-tech team be audit-ready?

A focused afternoon gets a first pack. Register your production clinical models, classify each under the EU AI Act, complete the ISO 42001 baseline questionnaire, seed the risk register with the clinical-AI starter template, and export. From there governance becomes a living workflow tied to your release process.

AI Governancefor Healthcare